






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
SCREAMING_SNAKE_CASE__				     =			(720, 1280)  # Height, Width
SCREAMING_SNAKE_CASE__				     =			(0.4, 0.6)  # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE__				     =			1 / 100
SCREAMING_SNAKE_CASE__				     =			''''''
SCREAMING_SNAKE_CASE__				     =			''''''
SCREAMING_SNAKE_CASE__				     =			''''''
SCREAMING_SNAKE_CASE__				     =			250

def        UpperCAmelCase__		(						):
			__a						,					__a						:  Optional[int]									=     get_dataset(lowerCamelCase_   ,  lowerCamelCase_				)
			for index in range(lowerCamelCase_				):
						__a						:  Optional[Any]									=     random.sample(range(len(lowerCamelCase_				)				)   ,  4				)
						__a						,					__a						,					__a						:  int									=     update_image_and_anno(
						    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  filter_scale=lowerCamelCase_   ,  )

						# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
						__a						:  Optional[Any]									=     random_chars(3_2				)
						__a						:  Tuple									=     path.split(os.sep				)[-1].rsplit('.'   ,  1				)[0]
						__a						:  Union[str, Any]									=     f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
						cva.imwrite(f'''{file_root}.jpg'''   ,  lowerCamelCase_   ,  [cva.IMWRITE_JPEG_QUALITY, 8_5]				)
						print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''				)
						__a						:  List[Any]									=     []
						for anno in new_annos:
									__a						:  Optional[int]									=     anno[3] - anno[1]
									__a						:  str									=     anno[4] - anno[2]
									__a						:  Tuple									=     anno[1] + width / 2
									__a						:  Optional[int]									=     anno[2] + height / 2
									__a						:  Tuple									=     f'''{anno[0]} {x_center} {y_center} {width} {height}'''
									annos_list.append(lowerCamelCase_				)
						with open(f'''{file_root}.txt'''   ,  'w'				) as outfile:
									outfile.write('\n'.join(line for line in annos_list				)				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       str				):
			__a						:  List[Any]									=     []
			__a						:  int									=     []
			for label_file in glob.glob(os.path.join(lowerCamelCase_   ,  '*.txt'				)				):
						__a						:  Union[str, Any]									=     label_file.split(os.sep				)[-1].rsplit('.'   ,  1				)[0]
						with open(lowerCamelCase_				) as in_file:
									__a						:  Tuple									=     in_file.readlines()
						__a						:  Optional[int]									=     os.path.join(lowerCamelCase_   ,  f'''{label_name}.jpg'''				)

						__a						:  Dict									=     []
						for obj_list in obj_lists:
									__a						:  List[Any]									=     obj_list.rstrip('\n'				).split(' '				)
									__a						:  Any									=     float(obj[1]				) - float(obj[3]				) / 2
									__a						:  Optional[int]									=     float(obj[2]				) - float(obj[4]				) / 2
									__a						:  int									=     float(obj[1]				) + float(obj[3]				) / 2
									__a						:  Any									=     float(obj[2]				) + float(obj[4]				) / 2

									boxes.append([int(obj[0]				), xmin, ymin, xmax, ymax]				)
						if not boxes:
									continue
						img_paths.append(lowerCamelCase_				)
						labels.append(lowerCamelCase_				)
			return img_paths, labels

def        UpperCAmelCase__		(						lowerCamelCase_       :       list   ,  lowerCamelCase_       :       list   ,  lowerCamelCase_       :       list[int]   ,  lowerCamelCase_       :       tuple[int, int]   ,  lowerCamelCase_       :       tuple[float, float]   ,  lowerCamelCase_       :       float = 0.0   ,  ):
			__a						:  Optional[int]									=     np.zeros([output_size[0], output_size[1], 3]   ,  dtype=np.uinta				)
			__a						:  Union[str, Any]									=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
			__a						:  List[str]									=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
			__a						:  List[str]									=     int(scale_x * output_size[1]				)
			__a						:  Dict									=     int(scale_y * output_size[0]				)

			__a						:  Optional[int]									=     []
			__a						:  Optional[Any]									=     []
			for i, index in enumerate(lowerCamelCase_				):
						__a						:  str									=     all_img_list[index]
						path_list.append(lowerCamelCase_				)
						__a						:  int									=     all_annos[index]
						__a						:  List[Any]									=     cva.imread(lowerCamelCase_				)
						if i == 0:  # top-left
									__a						:  List[Any]									=     cva.resize(lowerCamelCase_   ,  (divid_point_x, divid_point_y)				)
									__a						:  Dict									=     img
									for bbox in img_annos:
												__a						:  int									=     bbox[1] * scale_x
												__a						:  Any									=     bbox[2] * scale_y
												__a						:  int									=     bbox[3] * scale_x
												__a						:  str									=     bbox[4] * scale_y
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
						elif i == 1:  # top-right
									__a						:  Tuple									=     cva.resize(lowerCamelCase_   ,  (output_size[1] - divid_point_x, divid_point_y)				)
									__a						:  Dict									=     img
									for bbox in img_annos:
												__a						:  List[str]									=     scale_x + bbox[1] * (1 - scale_x)
												__a						:  Optional[Any]									=     bbox[2] * scale_y
												__a						:  Union[str, Any]									=     scale_x + bbox[3] * (1 - scale_x)
												__a						:  Dict									=     bbox[4] * scale_y
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
						elif i == 2:  # bottom-left
									__a						:  List[Any]									=     cva.resize(lowerCamelCase_   ,  (divid_point_x, output_size[0] - divid_point_y)				)
									__a						:  Optional[Any]									=     img
									for bbox in img_annos:
												__a						:  Optional[int]									=     bbox[1] * scale_x
												__a						:  Optional[Any]									=     scale_y + bbox[2] * (1 - scale_y)
												__a						:  Union[str, Any]									=     bbox[3] * scale_x
												__a						:  List[str]									=     scale_y + bbox[4] * (1 - scale_y)
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
						else:  # bottom-right
									__a						:  str									=     cva.resize(
									    lowerCamelCase_   ,  (output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
									__a						:  Tuple									=     img
									for bbox in img_annos:
												__a						:  Dict									=     scale_x + bbox[1] * (1 - scale_x)
												__a						:  str									=     scale_y + bbox[2] * (1 - scale_y)
												__a						:  Union[str, Any]									=     scale_x + bbox[3] * (1 - scale_x)
												__a						:  List[str]									=     scale_y + bbox[4] * (1 - scale_y)
												new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)

    # Remove bounding box small than scale of filter
			if filter_scale > 0:
						__a						:  Optional[Any]									=     [
						    anno
						    for anno in new_anno
						    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
						]

			return output_img, new_anno, path_list[0]

def        UpperCAmelCase__		(						lowerCamelCase_       :       int				):
			assert number_char > 1, "The number of character should greater than 1"
			__a						:  Dict									=     ascii_lowercase + digits
			return "".join(random.choice(lowerCamelCase_				) for _ in range(lowerCamelCase_				)				)


if __name__ == "__main__":
							main()
							print('''DONE ✅''')












'''simple docstring'''






import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


lowerCAmelCase									=   logging.getLogger(__name__)

if is_torch_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm
					import torch_xla.debug.metrics as met
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   *a_		,   a_=None		,   a_=None		,   a_=None		,   **a_						):
			super().__init__(*a_		,   **a_						)
			lowerCAmelCase  :     Tuple       						= eval_examples
			lowerCAmelCase  :     List[Any]       						= post_process_function
			lowerCAmelCase  :     List[Any]       						= quant_trainer_args
			lowerCAmelCase  :     Dict       						= 128  # default number of calibration samples



	def 	_lowerCamelCase     ( self		,   a_=None						):
			if calib_dataset is None and self.calib_dataset is None:
					raise ValueError("Trainer: calibration requires an calib_dataset."						)
			lowerCAmelCase  :     List[str]       						= calib_dataset if calib_dataset is not None else self.calib_dataset

			lowerCAmelCase  :     Union[str, Any]       						= self._remove_unused_columns(a_		,   description="Calibration"						)

			return DataLoader(
			    a_		,   batch_size=self.args.eval_batch_size		,   collate_fn=self.data_collator		,   drop_last=self.args.dataloader_drop_last		,   num_workers=self.args.dataloader_num_workers		,   pin_memory=self.args.dataloader_pin_memory		,   shuffle=a_		,   )



	def 	_lowerCamelCase     ( self		,   a_=None						):
			lowerCAmelCase  :     str       						= self.train_dataset if calib_dataset is None else calib_dataset
			lowerCAmelCase  :     str       						= self.get_calib_dataloader(a_						)

			lowerCAmelCase  :     Tuple       						= self.model
			quant_trainer.configure_model(a_		,   self.quant_trainer_args		,   calib=a_						)
			model.eval()
			quant_trainer.enable_calibration(a_						)

			logger.info("***** Running calibration *****"						)
			logger.info(F'''  Num examples = {self.calib_num}'''						)
			logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''						)

			for step, inputs in enumerate(a_						):
					# Prediction step
					lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= self.prediction_step(a_		,   a_		,   prediction_loss_only=a_						)
					if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
							break

			quant_trainer.finish_calibration(a_		,   self.quant_trainer_args						)
			lowerCAmelCase  :     Union[str, Any]       						= model



	def 	_lowerCamelCase     ( self		,   a_=None		,   a_=None		,   a_=None		,   a_ = "eval"						):
			lowerCAmelCase  :     Optional[Any]       						= self.eval_dataset if eval_dataset is None else eval_dataset
			lowerCAmelCase  :     Optional[Any]       						= self.get_eval_dataloader(a_						)
			lowerCAmelCase  :     Union[str, Any]       						= self.eval_examples if eval_examples is None else eval_examples

			# Temporarily disable metric computation, we will do it in the loop here.
			lowerCAmelCase  :     Dict       						= self.compute_metrics
			lowerCAmelCase  :     List[str]       						= None
			lowerCAmelCase  :     str       						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					lowerCAmelCase  :     Union[str, Any]       						= eval_loop(
					    a_		,   description="Evaluation"		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=a_		,   )
			finally:
					lowerCAmelCase  :     List[str]       						= compute_metrics

			if self.post_process_function is not None and self.compute_metrics is not None:
					lowerCAmelCase  :     Optional[Any]       						= self.post_process_function(a_		,   a_		,   output.predictions						)
					lowerCAmelCase  :     Optional[Any]       						= self.compute_metrics(a_						)

					# Prefix all keys with metric_key_prefix + '_'
					for key in list(metrics.keys()						):
							if not key.startswith(F'''{metric_key_prefix}_'''						):
									lowerCAmelCase  :     Any       						= metrics.pop(a_						)

					self.log(a_						)
			else:
					lowerCAmelCase  :     str       						= {}

			if self.args.tpu_metrics_debug or self.args.debug:
					# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
					xm.master_print(met.metrics_report()						)

			lowerCAmelCase  :     Dict       						= self.callback_handler.on_evaluate(self.args		,   self.state		,   self.control		,   a_						)
			return metrics



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_=None		,   a_ = "test"						):
			lowerCAmelCase  :     int       						= self.get_test_dataloader(a_						)

			# Temporarily disable metric computation, we will do it in the loop here.
			lowerCAmelCase  :     Union[str, Any]       						= self.compute_metrics
			lowerCAmelCase  :     List[str]       						= None
			lowerCAmelCase  :     Union[str, Any]       						= self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
			try:
					lowerCAmelCase  :     int       						= eval_loop(
					    a_		,   description="Prediction"		,   prediction_loss_only=True if compute_metrics is None else None		,   ignore_keys=a_		,   )
			finally:
					lowerCAmelCase  :     str       						= compute_metrics

			if self.post_process_function is None or self.compute_metrics is None:
					return output

			lowerCAmelCase  :     List[str]       						= self.post_process_function(a_		,   a_		,   output.predictions		,   "predict"						)
			lowerCAmelCase  :     Any       						= self.compute_metrics(a_						)

			# Prefix all keys with metric_key_prefix + '_'
			for key in list(metrics.keys()						):
					if not key.startswith(F'''{metric_key_prefix}_'''						):
							lowerCAmelCase  :     Optional[Any]       						= metrics.pop(a_						)

			return PredictionOutput(predictions=predictions.predictions		,   label_ids=predictions.label_ids		,   metrics=a_						)





	def 	_lowerCamelCase     ( self		,   a_="./"						):
			lowerCAmelCase  :     Optional[Any]       						= self.eval_dataset
			lowerCAmelCase  :     Optional[int]       						= self.get_eval_dataloader(a_						)

			lowerCAmelCase  :     Union[str, Any]       						= next(iter(a_						)						)

			# saving device - to make it consistent
			lowerCAmelCase  :     Any       						= torch.device("cuda" if torch.cuda.is_available() else "cpu"						)

			# convert to tuple
			lowerCAmelCase  :     str       						= tuple(v.to(a_						) for k, v in batch.items()						)

			logger.info("Converting model to be onnx compatible"						)
			from pytorch_quantization.nn import TensorQuantizer

			lowerCAmelCase  :     Any       						= True

			lowerCAmelCase  :     Dict       						= self.model.to(a_						)

			model.eval()
			model.float()

			lowerCAmelCase  :     Union[str, Any]       						= model.module if hasattr(a_		,   "module"						) else model
			quant_trainer.configure_model(a_		,   self.quant_trainer_args						)

			lowerCAmelCase  :     Any       						= os.path.join(a_		,   "model.onnx"						)
			logger.info(F'''exporting model to {output_model_file}'''						)

			lowerCAmelCase  :     List[Any]       						= {0: "batch_size", 1: "seq_len"}

			torch.onnx.export(
			    a_		,   a_		,   a_		,   export_params=a_		,   opset_version=13		,   do_constant_folding=a_		,   input_names=["input_ids", "attention_mask", "token_type_ids"]		,   output_names=["output_start_logits", "output_end_logits"]		,   dynamic_axes={
			        "input_ids": axes,
			        "attention_mask": axes,
			        "token_type_ids": axes,
			        "output_start_logits": axes,
			        "output_end_logits": axes,
			    }		,   verbose=a_		,   )
			logger.info("onnx export finished"						)



